Efficient dual source battery pack system for an electric vehicle

ABSTRACT

A method of optimizing the operation of the power source of an electric vehicle is provided, where the power source is comprised of a first battery pack (e.g., a non-metal-air battery pack) and a second battery pack (e.g., a metal-air battery pack). The power source is optimized to minimize use of the least efficient battery pack (e.g., the second battery pack) while ensuring that the electric vehicle has sufficient power to traverse the expected travel distance before the next battery charging cycle.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 12/962,693, filed Dec. 8, 2010, and claims benefitof the filing date of U.S. Provisional Patent Application Ser. No.61/372,351, filed Aug. 10, 2010, the disclosures of which areincorporated herein by reference for any and all purposes.

FIELD OF THE INVENTION

The present invention relates generally to batteries and, moreparticularly, to means for optimizing the power source of an electricvehicle that utilizes battery packs of differing types.

BACKGROUND OF THE INVENTION

A metal-air cell is a type of battery that utilizes the same energystorage principles as a more conventional cell such as a lithium ion,nickel metal hydride, nickel cadmium, or other cell type. Unlike suchconventional cells, however, a metal-air cell utilizes oxygen as one ofthe electrodes, typically passing the oxygen through a porous metalelectrode. The exact nature of the reaction that occurs in a metal-airbattery depends upon the metal used in the anode and the composition ofthe electrolyte. Exemplary metals used in the construction of the anodeinclude zinc, aluminum, magnesium, iron, lithium and vanadium. Thecathode in such cells is typically fabricated from a porous structurewith the necessary catalytic properties for the oxygen reaction. Asuitable electrolyte, such as potassium hydroxide in the case of azinc-air battery, provides the necessary ionic conductivity between theelectrodes while a separator prevents short circuits between the batteryelectrodes.

Due to the use of oxygen as one of the reactants, metal-air cells havesome rather unique properties. For example, since the oxygen does notneed to be packaged within the cell, a metal-air cell typically exhibitsa much higher capacity-to-volume, or capacity-to-weight, ratio thanother cell types making them an ideal candidate for weight sensitiveapplications or those requiring high energy densities.

While metal-air cells offer a number of advantages over a conventionalrechargeable battery, most notably their extremely high energy density,such cells also have a number of drawbacks. For example, care must betaken to avoid the undesired evaporation of electrolyte, especially inhigh temperature, low humidity environments. It is also necessary toensure that there is a sufficient supply of air to the cells duringdischarge cycles, and means for handling the oxygen emitted from thecells during the charge cycles. Another potential disadvantage of ametal-air cell is the power available on discharge. Due to the kineticsof the electrode reactions, the maximum discharge rate is far lower thanthat of many other types of cells, such as lithium-ion cells.

Accordingly, while metal-air cells offer some intriguing benefits, suchas their high energy densities, their shortcomings must be taken intoaccount in order to successfully integrate the cells into a system. Thepresent invention provides such a system by combining a metal-airbattery pack with a conventional battery pack in order to gain thebenefits associated with each battery type.

SUMMARY OF THE INVENTION

The present invention provides a method of optimizing the operation ofthe power source of an electric vehicle, the power source comprised of afirst battery pack (e.g., a non-metal-air battery pack) and a secondbattery pack (e.g., a metal-air battery pack). The power source isoptimized to minimize use of the least efficient battery pack (e.g., thesecond battery pack) while ensuring that the electric vehicle hassufficient power to traverse the expected travel distance before thenext battery charging cycle. Further optimization may be achieved bysetting at least one acceleration limit and/or at least one maximumspeed limit based on vehicle efficiency and the state-of-charge (SOC) ofthe first and second battery packs.

In at least one embodiment of the invention, a method of optimizing thepower source of an electric vehicle is provided, the power sourceincluding at least a first battery pack (e.g., non-metal-air batterypack) and a second battery pack (e.g., metal-air battery pack), themethod including the steps of (a) determining the SOC of the first andsecond battery packs; (b) determining vehicle efficiency; (c) obtainingthe expected distance to travel before the next charging cycle; (d)determining the optimal split between the first and second battery packsto minimize use of the second battery pack while still providing theelectric vehicle sufficient power to traverse the expected traveldistance; and (e) providing power to the electric vehicle in accordancewith the optimal split. The step of determining the optimal split mayfurther comprise the step of maintaining a minimum SOC within the firstbattery pack. The step of determining the optimal split may furthercomprise the step of maximizing power source efficiency. The method mayfurther include the steps of (f) monitoring current SOC for the firstand second battery packs; (g) comparing the current first and secondbattery pack SOCs to first and second battery pack predicted useprofiles; (h) determining a revised optimal split if the current firstbattery pack SOC does not approximately match the first battery packpredicted use profile or the current second battery pack SOC does notapproximately match the second battery pack predicted use profile; (i)and providing power to the electric vehicle in accordance with therevised optimal split. The method may further include the steps of (f)monitoring current SOC for the first and second battery packs; (g)determining remaining SOC for the first and second battery packs; (h)comparing the remaining first and second battery pack SOCs to first andsecond battery pack predicted use profiles; (i) determining a revisedoptimal split if the remaining first battery pack SOC does notapproximately match the first battery pack predicted use profile or theremaining second battery pack SOC does not approximately match thesecond battery pack predicted use profile; (i) and providing power tothe electric vehicle in accordance with the revised optimal split. Themethod may further include the steps of monitoring traffic conditionsand adjusting vehicle efficiency based on the traffic conditions. Themethod may further include the step of determining battery packoperational parameters prior to determining the optimal split. Thevehicle efficiency may be (i) the average conversion efficiency for theelectric vehicle; (ii) given as a function of vehicle speed and vehicleacceleration; and/or (iii) corresponding to a particular driver. Thetravel distance may be determined from (i) a preset distance; (ii) adestination input into the vehicle's navigation system; or (iii) atravel itinerary input into the vehicle's navigation system. The methodmay further include the steps of estimating vehicle elevation variationsor traffic conditions based on a travel itinerary input into thevehicle's navigation system and adjusting the vehicle efficiency basedon the elevation variations or traffic conditions. The method mayfurther include the steps of determining ambient temperature, estimatingbattery pack cooling demands based on the ambient temperature, andadjusting vehicle efficiency based on the cooling demands. The methodmay further include the steps of estimating vehicle weight and adjustingvehicle efficiency based on vehicle weight. The method may furtherinclude the steps of determining ambient lighting conditions, estimatingdriving light requirements based on ambient lighting conditions,estimating battery pack loading to meet driving light requirements, andadjusting vehicle efficiency based on battery pack loading. The methodmay further include the steps of determining weather conditions andadjusting vehicle efficiency based on the weather conditions. The methodmay further include the steps of identifying the driver, estimatingauxiliary battery pack loading corresponding to the driver, andadjusting vehicle efficiency based on the estimated auxiliary batterypack loading.

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the primary components of an electric vehicle thatutilizes both a metal-air battery pack and a conventional battery pack;

FIG. 2 illustrates the basic methodology of the invention;

FIG. 3 illustrates the methodology of a preferred embodiment;

FIG. 4 illustrates a specific embodiment of the methodology of FIG. 3;

FIG. 5 illustrates a modification of the methodology of FIG. 3, whereinthe controller sets acceleration limits during the battery packoptimization routine;

FIG. 6 illustrates a modification of the methodology of FIG. 5, whereinthe controller activates a warning when the acceleration limit isreached/exceeded;

FIG. 7 illustrates a modification of the methodology of FIG. 3, whereinthe controller sets maximum speeds during the battery pack optimizationroutine;

FIG. 8 illustrates a modification of the methodology of FIG. 7, whereinthe controller activates a warning when the maximum speed isreached/exceeded;

FIG. 9 illustrates a modification of the methodology of FIG. 3, whereinduring the battery pack optimization routine the controller sets maximumspeeds based on the speed limit corresponding to the vehicle's location;

FIG. 10 illustrates a modification of the methodology of FIG. 9, whereinthe controller activates a warning when the maximum speed isreached/exceeded;

FIG. 11 illustrates a modification of the methodology of FIG. 9, whereinthe maximum speeds set by the controller are utilized by the cruisecontrol system;

FIG. 12 illustrates a modification of the methodology of FIG. 9, whereinthe controller overrides the maximum speeds set by the controller toensure that the vehicle arrives at a predefined destination at apredefined arrival time; and

FIG. 13 illustrates a methodology combining the attributes of theprocesses illustrated in FIGS. 6 and 8.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

In the following text, the terms “battery”, “cell”, and “battery cell”may be used interchangeably. The term “battery pack” as used hereinrefers to one or more individual batteries that are electricallyinterconnected to achieve the desired voltage and capacity for aparticular application, the individual batteries typically containedwithin a single piece or multi-piece housing. The term “electricvehicle” as used herein refers to either an all-electric vehicle, alsoreferred to as an EV, plug-in hybrid vehicles, also referred to as aPHEV, or a hybrid vehicle (HEV), a hybrid vehicle utilizing multiplepropulsion sources one of which is an electric drive system. It shouldbe understood that identical element symbols used on multiple figuresrefer to the same component, or components of equal functionality.Additionally, the accompanying figures are only meant to illustrate, notlimit, the scope of the invention and should not be considered to be toscale.

Given the high energy density and the large capacity-to-weight ratiooffered by metal-air cells, they are well suited for use in electricvehicles. Due to their limited power density, however, their use is mostappropriate when combined with a more conventional power source, such asa lithium ion battery pack. This aspect is illustrated in FIG. 1 whichshows the primary components of an EV 100 that utilizes both a metal-airbattery pack 101 and a conventional, non-metal-air battery pack 103. Asused herein, metal-air batteries refer to any cell that utilizes oxygenas one of the electrodes and metal (e.g., zinc, aluminum, magnesium,iron, lithium, vanadium, etc.) in the construction of the otherelectrode. Conventional battery pack 103 utilizes non-metal-air cells,and preferably ones that provide high power density, thus providing acombined power source that achieves an optimal combination of energy andpower. Exemplary batteries used in conventional battery pack 103include, but are not limited to, lithium ion (e.g., lithium ironphosphate, lithium cobalt oxide, other lithium metal oxides, etc.),lithium ion polymer, nickel metal hydride, nickel cadmium, nickelhydrogen, nickel zinc, silver zinc, etc. In a preferred application,battery packs 101 and 103 are coupled to one or more drive motors 105that provide propulsion to one or more wheels of EV 100. A controller107 optimizes the vehicle's dual power source, i.e., battery packs 101and 103, in light of the current battery pack conditions (e.g.,state-of-charge, temperature, etc.), preferred battery packcharge/discharge conditions, and the various operating conditions.Exemplary operating conditions include those placed on the system by theuser (e.g., distance, speed, acceleration, etc.), road conditions (e.g.,uphill, downhill, traffic, etc.), charging system (e.g., availablepower, available time for charging, etc.), and environmental conditions(e.g., ambient temperature, humidity, etc.).

FIG. 2 illustrates the basic application of the dual source batterysystem of the invention. As shown, during the discharge cycle 201 one orboth battery packs 101 and 103 provide energy to the intendedapplication (e.g., propulsion, cooling, auxiliary systems, etc.), theflow of energy represented by paths 203/204. Similarly, during thecharging cycle 205 one or both battery packs 101 and 103 receive energyfrom a charging source, not shown, the flow of energy represented bypaths 207/208. The charging source may be an external power source(e.g., power grid) or an internal power source (e.g., regenerativesystem). Lastly, in some embodiments energy may be transferred directlybetween battery packs 101 and 103 as represented by energy flow pathway209.

In accordance with the invention, and as illustrated in system 200,controller 107 controls the flow of energy to and from both themetal-air battery pack 101 and the non-metal-air battery pack 103. Asdescribed in detail below, the methodology applied by controller 107 isbased on the input from a variety of sensors 211 as well as the currentoperating conditions (e.g., temperature and state-of-charge (SOC), etc.)of both battery packs.

The primary advantage of using two different types of battery packs, andmore specifically, a metal-air battery pack 101 and a conventional,non-metal-air battery pack 103, is that as the operationalcharacteristics of the two battery types are quite different, an EVutilizing both battery types can be designed to take advantage of thebenefits of both battery types, while significantly limiting thenegative effects of either type.

While the specific operating requirements and characteristics of the twobattery packs will depend upon the particular chemistries of the cellsselected for each battery pack, the basic differences between the twotypes are provided below, thus further clarifying how the presentinvention utilizes both battery types to optimize operation of thecombined power source.

-   -   Energy Density—The energy density of metal-air cells is very        high, even relative to high density non-metal-air cells such as        lithium-ion cells. In general, this is the result of the        metal-air cells utilizing oxygen, contained within the air, as        one of the reactants, thus reducing cell weight and increasing        energy density. Accordingly, in weight sensitive applications        such as EVs, metal-air cells offer a distinct advantage over        non-metal-air cells in terms of energy density.    -   Power Density—The power density of a cell is determined by the        cell's reaction kinetics. Currently the chemistries, materials        and configurations used in metal-air cells provide a lower power        density and discharge rate than that achieved by many        non-metal-air cells. While the lower power density and discharge        rate is adequate for many applications, it is lower than desired        for more demanding applications. As a result, by combining both        cell types in a single application as presently described, the        high energy density, moderate power density metal-air cells can        provide a baseline power source while the moderate energy        density, high power density non-metal-air cells can provide the        necessary power for peak loads, for example the loads that may        be experienced during acceleration, high speed, and hill        climbing. Clearly the relative sizes allocated for each battery        type/pack within an EV depends upon the configuration and design        of the vehicle (i.e., vehicle weight; performance, range and        cost goals; etc.).    -   Optimal Charge/Discharge Temperatures—Temperature affects many        critical characteristics of battery operation regardless of the        battery type/chemistry. Exemplary characteristics affected by        temperature include cell voltage and discharge capacity, cell        impedance, cell life, non-recoverable capacity loss (at high        temperatures), and charging efficiency. While the preferred and        optimal charge and discharge characteristics depend upon the        particular cell design, chemistry, and reaction kinetics, in        general metal-air cells may be charged and discharged over a        wider temperature range than non-metal-air cells without unduly        affecting cell life and efficiency.    -   State-of-Charge (SOC)—The depth of discharge reached during the        discharge cycle, and the level that a cell is charged (up to        100%) during the charge cycle, may dramatically affect the        performance and life characteristics of a cell. These        characteristics are dependent upon cell design and chemistry.    -   Charging Characteristics—By definition a rechargeable battery is        rechargeable, however, the number of times that a cell may be        recharged without substantially affecting the capabilities and        lifetime of the cell vary greatly with cell design and        chemistry. In general, however, current state-of-the-art        metal-air cells are not capable of being recharged as many times        as a non-metal-air cell without causing a significant        degradation in lifetime and capacity. Additionally, the charging        efficiency of current metal-air cells is typically worse than        that associated with non-metal-air cells.

In order to improve upon the efficiency of an electric vehicle that usesa dual power source, i.e., a power source utilizing two different typesof batteries, the present invention minimizes the use of the leastefficient of the two battery types. Thus, for example, assuming a powersource that utilizes both a first battery pack comprised ofnon-metal-air cells and a second battery pack comprised of metal-aircells, during vehicle operation the present invention minimizes thedischarge of the metal-air cells. By minimizing the use of the metal-aircells, the effects of this cell's lower power density and inferior cyclelife and charge efficiency are minimized. It should be understood,however, that the relative performance of metal-air and non-metal-aircells with respect to power density, cycle life and charge efficiencymay be reversed at some point in the future due, for example, to futureimprovements in metal-air cell chemistry and design. If such a reversalin performance were to occur, it will be appreciated that thecomposition of the first and second battery packs of the invention wouldbe reversed, thus still optimizing the vehicle's efficiency.

FIG. 3 illustrates the basic methodology associated with a preferredembodiment of the invention. The illustrated process assumes, aspreviously noted, a power source comprised of at least two battery packsof different battery types, e.g., a first battery pack comprised ofnon-metal-air cells and a second battery pack comprised of metal-aircells. As shown, once vehicle operation is initiated (step 301), thestate-of-charge (SOC) of both non-metal-air battery pack 103 andmetal-air battery pack 101 are determined (step 303). Note that in thefigures “non-metal-air” is abbreviated as “NMA” and “metal-air” isabbreviated as “MA”. In step 305 the system determines the efficiency ofthe vehicle. In this context, efficiency refers to the vehicle'sefficiency in converting the output from the vehicle's power source,i.e., from each of the battery packs comprising the dual battery packsource, to actual miles traveled, and is given in terms ofwatt-hours/mile or equivalent metric units. The vehicle's efficiency isdriven by both the electrical conversion efficiency and the thermaldissipation characteristics of each of the battery packs, each varyingas a function of speed and acceleration. It will be appreciated that theability of the present invention to optimize use of the vehicle's dualbattery packs depends, in part, on the accuracy of the efficiency inputinto the system controller and there are clearly numerous techniques fordetermining and/or inputting efficiency into the system. For example,the efficiency may be based on an average conversion efficiency, or theefficiency may be comprised of a look-up table (or similar form of datainput) that provides the instantaneous efficiency as a function of speedand acceleration. Additionally, the efficiency data may be provided forthe particular make and model of the vehicle; alternately, based onefficiency data for the particular vehicle in question; alternately,based on efficiency data gathered for the particular vehicle in questionand for a relatively recent period of time (e.g., for the last month orweek; for the last 100 miles or 50 miles the vehicle was driven; etc.).In at least one embodiment, the efficiency data is stored in a memory(e.g., EPROM, EEPROM, flash memory, RAM, a solid state disk drive, ahard disk drive, or any other memory type or combination of memorytypes) associated with controller 107. The memory may also be used tostore other system operating parameters as well as process instructions.

Next, in step 307, the intended distance for vehicle 100 to travelbefore the next, expected charging cycle is input into the system. Asdescribed in detail below, this distance may be input directly into thesystem by the user (e.g., driver); alternately, the distance may beinput indirectly by the user's interaction with the vehicle's navigationsystem; alternately, the distance may be based on historical data;alternately, the distance may be based on a preset set of requirementsand assumptions. Clearly the extent to which the system may optimize useof the vehicle's dual battery packs depends on the accuracy of thedistance input into the controller in step 307.

In step 309, controller 107 determines the optimal split of battery packusage based on the distance to travel before recharging and on thecalculated power required to reach that distance. The required powerdepends on the efficiency of the vehicle, provided in step 305, inconverting battery power to propulsive power. As such, the requiredpower is based on a number of assumptions, such as vehicle speed,terrain (e.g., uphill, downhill, or relatively flat), vehicle weight(e.g., number of passengers, cargo, etc.), wind effects (e.g., headwind,tailwind) and the temperature since the temperature may affect batteryefficiency. Additionally, determining the required power is based onassumptions relating to the use of various auxiliary systems that alsorequire power from the vehicle's batteries (e.g., radio, passengerheating/cooling (HVAC) system, battery heating/cooling, headlights,etc.). These assumptions may be stored in memory or calculated based ondetectable criteria (e.g., vehicle weight, temperature, etc.). If theassumptions are preset and stored in memory, the presets may be input bythe vehicle's manufacturer, a representative of the manufacturer, athird party service provider, or the user. Regardless of the techniqueused to determine these assumptions, the assumptions are input intocontroller 107 at step 311. In addition to the SOC for each of thebattery packs (step 303), the vehicle's efficiency (step 305), theintended distance (step 307) and the operational assumptions (step 311),battery pack operational parameters are also input into the controller(step 313) prior to determining battery pack split. Battery packparameters that may be input in step 313 include capacity, performanceversus temperature data, and minimum allowable SOC levels.

As previously noted, the output from step 309 is the calculated splitbetween the battery packs as required to optimize vehicle efficiency(step 315). In the preferred embodiment utilizing non-metal-air andmetal-air battery packs, and based on the efficiencies andcharacteristics of different battery types in accordance with thecurrent state-of-the art, optimization step 309 minimizes use of themetal-air battery pack while ensuring that a minimum SOC level, i.e., apower reserve, is retained within the non-metal-air battery pack at theconclusion of the trip, i.e., immediately prior to battery charging.Accordingly, in a typical embodiment during the initial stages ofvehicle use, the non-metal-air battery pack is used exclusively. Then,as the SOC of the non-metal-air battery pack drops, the metal-airbattery pack is used to augment the output of the non-metal-air batterypack.

Since the system of the invention is designed to maximize power sourceefficiency while ensuring that a sufficient power reserve is maintainedwithin the non-metal-air battery pack, in step 317 controller 107monitors the SOC of the battery packs, comparing those SOC values to theSOC values predicted by controller 107 in step 309 (i.e., predicted useprofiles for the two battery packs). If the actual and predicted SOCvalues are the same, within a preset tolerance (step 319), then powercontinues to be split in accordance with the original optimizationscheme. If, on the other hand, the actual SOC levels do not match upwith the predicted levels (step 321), then the data input intocontroller 107 is updated (step 323) and the controller recalculates theoptimal split between the battery packs. At a minimum, during step 323updated SOC levels are input into controller 107 as well as theremaining distance to travel.

As previously noted, the optimal split between the non-metal-air andmetal-air battery packs determined in step 309 depends upon the specificchemistry, configuration, and capacity of the battery packs. To furtherillustrate the invention, FIG. 4 provides a specific embodiment based onthe assumptions (i) that the non-metal-air battery pack has sufficientcapacity, when fully charged, to meet the normal driving requirements ofthe vehicle; (ii) the non-metal-air battery pack is more energyefficient than the metal-air battery pack; and (iii) the metal-airbattery pack is more susceptible to performance degradation due torepeated charge/discharge cycling than the non-metal-air battery pack.

As shown in FIG. 4, after vehicle operation is initiated (step 301), thevarious operational parameters are input into the system (step 401)including SOC for each of the battery packs (step 303), vehicleefficiency (step 305), travel distance (step 307), system usageassumptions (step 311) and battery pack parameters (step 313).Controller 107 then determines whether or not the non-metal-air batterypack has sufficient charge to meet the driving requirements placed onthe system, including the expected travel distance, while retainingsufficient back-up reserve SOC (step 403). If it does, then only thenon-metal-air battery pack is used (steps 405 and 407). If thenon-metal-air battery pack does not have sufficient charge to meet thedriving requirements, then controller 107 determines an optimal splitbetween the two battery packs based on the driving requirements (step409) and provides power to the vehicle systems based on that optimalsplit (step 411). In steps 413 and 415, controller 107 monitors the SOCof the battery packs and compares those SOC values to the SOC valuespredicted by controller 107. If the actual and predicted SOC values arethe same within a preset tolerance (steps 417 and 419), then powercontinues to be split in accordance with the original optimizationscheme (i.e., steps 405 or 409). If during step 421 controller 107determines that extra power is required beyond the capabilities of thenon-metal-air battery pack (step 423), for example due to the systemdrain on the non-metal-air battery pack being greater than expected(e.g., because of unexpected headwinds, excess cargo weight, etc.), thenthe operating conditions are updated (step 425) and controller 107determines an optimal split between the battery packs. Similarly, ifduring step 427 the actual SOC levels do not match up with the predictedlevels (step 429), then the data input into controller 107 is updated(step 425) and the controller recalculates the optimal split between thebattery packs.

While the process illustrated in FIG. 3 can be used to provide theoptimal split between the two battery packs comprising the vehicle'spower source, further optimization may be achieved by allowingcontroller 107 to define or set the optimal vehicle speed and/or vehicleacceleration since vehicle efficiency is a function of vehicle speed andacceleration.

FIG. 5 illustrates a process based on the methodology of FIG. 3. Asshown, in addition to setting the optimal split between thenon-metal-air and metal-air battery packs (step 309), controller 107also sets acceleration limits (step 501). In at least one embodiment,the acceleration limit is set as a motor torque limit. In oneembodiment, acceleration limits are preset and applied whenever theoptimization system is used. In an alternate embodiment, when the powersource optimization system is used the user may choose whether or not toallow acceleration limits to be set by the controller. In yet anotherembodiment, the controller sets soft acceleration limits during powersource optimization. As used herein, a soft limitation refers to alimitation that is advisory in nature and therefore allows the user toeasily override the limitation. For example, when controller 107 sets asoft acceleration limit, the system can be configured to send a warningto the driver whenever the driver is approaching the acceleration limitor whenever the acceleration limit is exceeded. Warnings may be in theform of a warning light (e.g., in the instrument cluster or the userinterface), an audible warning (e.g., an audible alarm or a pre-recordedvocal message), or a physical response (e.g., increasing acceleratorpedal resistance). FIG. 6 illustrates a system utilizing softacceleration limits, this process including the steps of monitoring theacceleration and comparing it to the preset limits (step 601), andactivating a warning such as those described above when the limit isexceeded (step 603).

The acceleration limits applied in the process illustrated in FIG. 5 maybe constant, i.e., applied equally regardless of the current vehiclespeed or the SOC of one or both battery packs. Alternately, theacceleration limits may be based on the remaining SOC of one or bothbattery packs, for example applying more restrictive acceleration limitsas the remaining SOC decreases, thereby conserving more battery packpower. Alternately, the acceleration limits may be based on the currentvehicle speed, for example applying more restrictive acceleration limitsas the vehicle speed increases. Alternately, the acceleration limits maybe tied to some other vehicle operating parameter such as motor torque.

As previously noted, in addition to optimizing the split between the twobattery packs comprising the vehicle's power source, furtheroptimization may be achieved by allowing controller 107 to set theoptimal vehicle speed, or at least to set maximum vehicle speeds, sincevehicle efficiency is a function of vehicle speed and, in general,decreases as vehicle speed increases.

FIG. 7 illustrates a process based on the methodology of FIG. 3. Asshown, in addition to setting the optimal split between thenon-metal-air and metal-air battery packs (step 309), controller 107also sets a maximum speed (step 701). In its simplest configuration, thesystem sets the legal speed limit, or a percentage thereof (e.g., 95% ofthe legal speed limit), as the maximum speed in step 701. In alternateconfigurations, and as described more fully below, the maximum speed setby the system varies depending upon the vehicle's current location.

In one embodiment, the speed limit is preset and applied whenever theoptimization system is used. In an alternate embodiment, when the powersource optimization system is used the user may choose whether or not toallow the controller to set a speed limit. In yet another embodiment,the controller sets a soft speed limit during power source optimization,thus allowing the user to easily override the limitation if desired. Forexample, when controller 107 sets a soft speed limit, the system can beconfigured to send a warning to the driver whenever the driver isapproaching the speed limit or whenever the speed limit is exceeded.Warnings may be in the form of a warning light (e.g., in the instrumentcluster or the user interface), an audible warning (e.g., an audiblealarm or a pre-recorded vocal message), or a physical response (e.g.,increasing accelerator pedal resistance). FIG. 8 illustrates a systemutilizing soft speed limits, this process including the steps ofmonitoring the speed of the vehicle and comparing it to the preset limit(step 801), and activating a warning such as those described above whenthe limit is exceeded (step 803).

Preferably the speed limit set by controller 107 is constant.Alternately, the speed limit may be based on the remaining SOC of one orboth battery packs, for example applying a more restrictive speed limitas the remaining SOC decreases, thereby conserving more battery packpower.

In an alternate configuration, illustrated in FIG. 9, in addition tooptimizing the power split between battery packs, controller 107monitors vehicle position (step 901) and sets a maximum speed (step 903)based on the vehicle's location and the speed limit for that location.Vehicle position is determined using an on-board global positioningsystem (GPS), either a stand-alone subsystem or the GPS that correspondsto the vehicle's navigation system. Preferably the GPS includes a database of speed limit as a function of location. Alternately, the speedlimit may be derived from the type of road, e.g., interstate highways,city roads, etc.

The speed limit set in step 903 may be based on a percentage of thelegal speed limit for the vehicle's current location, e.g., 95% of thespeed limit. Alternately, controller 107 may set the speed limit basedon the vehicle's current location and the vehicle's efficiency datagiven as a function of speed. For example, in this configurationcontroller 107 may set the maximum speed to be equal to the speed limitat vehicle speeds of 20 mph or less; 98% of the legal speed limit atvehicle speeds of 20-40 mph; 95% of the legal speed limit at vehiclespeeds of 40-55 mph; and 92% of the legal speed limit at vehicle speedsgreater than 55 mph.

As in the prior embodiments, when the present system is used to optimizeboth battery pack usage and set maximum speeds based on the speed limitfor the vehicle's location, the process can either utilize hard maximumspeed limits (e.g., FIG. 9), or soft maximum speed limits (e.g., FIG.10) in which the vehicle speed is compared to the preset limit (step1001) and a warning is activated when the limit is exceeded (step 1003).

In an alternate configuration, illustrated in FIG. 11, in addition todetermining the vehicle's location (step 901) and setting the maximumspeed based on the speed limit for the vehicle's location (step 903),the vehicle's cruise control utilizes the speed set by the controller(step 1101) to aid the driver in achieving optimal efficiency. As such,whenever the user activates the cruise control, the speed is held to themaximum speeds set by controller 107 in step 903. As with the processdescribed above relative to FIGS. 9 and 10, the speed limit set in step903 is preferably a percentage of the legal speed limit for thevehicle's current location, as provided by an on-board GPS system. Itwill be appreciated that the driver may override the cruise controlwhenever desired, either exceeding the maximum speeds set in the cruisecontrol system, or slowing down as the driver deems necessary.

In the systems described above relative to FIGS. 7-11, the maximum speedis set based on preset maximums or based on a percentage of the legalspeed limit, e.g., the speed limit for the vehicle's current location.In an alternate configuration, illustrated in FIG. 12, controller 107sets speeds based both on the legal speed limit for the vehicle'spresent location, and on input provided by the user as to when the usermust arrive at a predetermined location (step 1201). In addition toinputting the required arrival time, the user also inputs thedestination (step 1203). The destination input in step 1203 may be thesame as, or different from, the distance information input in step 307.Regardless of whether the two distances are the same or different, instep 315 controller 107 determines the optimal split between thenon-metal-air and metal-air battery packs to assure that the vehicle cantravel the distance input in step 307 while retaining the desiredreserves within the non-metal-air battery pack at the conclusion of thetrip, i.e., immediately prior to battery charging. As in priorembodiments, controller 107 sets the speed in step 903 to optimizeefficiency while still traveling at an adequate speed to be safe. Assuch, typically the maximum speed set in step 903 is a percentage of thelegal speed limit for the vehicle's present location (e.g., 90 or 95% ofthe legal speed limit). Additionally, in this configuration during step1205 controller 107 overrides the speed set in step 903 in order toensure that the vehicle arrives at the destination input in step 1203 bythe arrival time input in step 1201. Once the destination input in step1203 has been reached, and assuming further travel is required prior torecharging, the maximum speed defaults back to that set in step 903.

It will be appreciated that controller 107 may set the acceleration, forexample as illustrated in FIGS. 5 and 6, as well as the maximum speeds,for example as illustrated in FIGS. 7-12. An exemplary system combiningthe acceleration optimizing configuration illustrated in FIG. 6 and thespeed optimizing configuration illustrated in FIG. 8 is shown in FIG.13. It will be appreciated that inventors envision other combinations ofother embodiments, e.g., combining the configurations of FIGS. 5 and 7;combining the configurations of FIGS. 6 and 9; combining theconfigurations of FIGS. 6 and 10; combining the configurations of FIGS.6 and 11; combining the configurations of FIGS. 6 and 12; etc.

As previously noted, the present invention optimizes the power source ofan electric vehicle comprised of two different types of batteries basedon the requirements placed on the power source by the vehicle and itsdriver. As such, the optimization system requires various input relatingto the capabilities and conditions corresponding to the battery pack,the efficiency of the vehicle in converting the output from the powersource to miles traveled, the ambient environment, and the driver'sexpectations regarding future travel plans. While much of this wasdescribed above, further details are provided below.

-   -   Vehicle Efficiency—As noted above, the ability of the present        invention to optimize use of the vehicle's dual battery packs        depends, in part, on the accuracy of the efficiency data input        into the system controller in step 305. The efficiency data        input into the optimization process may be based on the average        conversion efficiency for the particular make and model of the        vehicle; more preferably the efficiency input in step 305 may be        based on the average conversion efficiency for the particular        vehicle undergoing power source optimization; and still more        preferably the efficiency input in step 305 may be based on the        conversion efficiency data for the particular vehicle undergoing        power source optimization during a relatively recent period of        time (e.g., for the last month or week; for the last 100 miles        or 50 miles the vehicle was driven; etc.). In at least one        embodiment, efficiency data is recorded and stored within the        vehicle system memory (e.g., memory corresponding to controller        107) for each individual driver. Then, during step 305, the        current driver's identification is either input or determined by        the controller. The driver's identification may be input using a        touch-screen, via the navigation system interface, or via some        other vehicle user interface. Alternately, the driver's        identification may be determined by controller 107 using data        received from the driver's keyfob or similar device. Using        efficiency data in step 305 that is specific to a particular        driver allows the optimization system to take into account a        particular user's driving habits (e.g., acceleration from stops;        acceleration/deceleration versus steady speed driving; top        speeds; etc.), thus making the optimization process more        accurate.    -   Distance—As noted above, the ability of the present invention to        optimize use of the vehicle's dual battery packs depends, in        part, on the accuracy of the distance information input into the        controller in step 307 as this distance information allows the        controller to optimize battery pack usage while ensuring that        sufficient reserve power is maintained. Accordingly, rough        estimates of expected travel distance, such as when the user        inputs a ‘best guess’ as to travel distance, provide the least        accuracy in power source optimization. Similarly, if the system        utilizes a system preset distance, optimization will typically        be hampered. System preset distances may be input by the        vehicle's manufacturer or representative, a third party service        provider, or, as preferred, the user. Preferably the user inputs        as a preset distance the typical driving distance between charge        cycles, e.g., the distance traveled to/from work on a daily        basis. In this case, using a preset distance simplifies the        optimization process by allowing the user to use a preset based        on a ‘typical’ driving pattern. In at least one preferred        embodiment, the distance input during step 307 is input by the        user via interaction with the vehicle's navigation system,        rather than a separate interface. For example, the user can        input a travel destination and indicate that a round trip to the        travel destination will be required prior to battery charging.        Alternately, the user may input an entire travel itinerary into        the navigation system, the travel itinerary including multiple        travel destinations for the next driving cycle. In at least one        embodiment of the invention, if the user does not input a        specific travel distance, the system defaults to using        historical data. For example, the system may be configured to        average the travel distance between charge cycles for a        predefined number of cycles. In at least one embodiment of the        invention, if user identification is input into the system,        either directly or indirectly, the system defaults to either        preset travel distance or historical data for that particular        driver.

In at least one preferred embodiment, if controller 107 has access tospecific route information, for example due to the user inputting aspecific destination or travel itinerary into the vehicle's navigationsystem during step 307, then the vehicle efficiency data input in step305 is fine tuned. Specifically, in this instance the vehicle efficiencydata may be adjusted to take into account expected travel speeds andexpected elevation changes, both of which may affect vehicle efficiency.

-   -   Operational Details—In steps 311 and 313, various operational        details may be input into, or derived by, the system, these        details further improving on the accuracy of the vehicle        efficiency, and thus the optimization process of the invention.        First, the operating parameters of each of the battery packs may        be monitored, thus allowing the controller to adjust power        source operation to further optimize battery pack usage.        Exemplary battery pack parameters that may be monitored include        the temperature of each of the battery packs (e.g., cell        temperatures within each pack; coolant temperatures; etc.),        current discharged from each battery pack, and operational        parameters specific to the metal-air cells (e.g., oxygen        concentration, humidity, air flow temperature, rate, etc.).        Second, by monitoring ambient temperature, the controller can        adjust the vehicle efficiency based on the expected cooling or        heating required by the battery packs. Additionally, controller        107 can estimate the likely use of the vehicle's HVAC system        based on the ambient temperature, HVAC use clearly tied to        battery drain and thus vehicle efficiency. Third, by monitoring        vehicle weight, or requesting passenger/cargo information from        the driver, controller 107 is able to estimate effects of        vehicle weight on vehicle efficiency (e.g., more passengers        and/or cargo equates to a heavier vehicle which can decrease        vehicle efficiency). Fourth, by monitoring vehicle elevation or        estimating elevation based on the travel itinerary input into        the vehicle's navigation system, vehicle efficiency can be        adjusted (e.g., driving uphill requires more energy, and is        therefore less efficient, than driving downhill). Fifth, by        monitoring ambient lighting controller 107 can determine whether        or not driving lights are required and adjust vehicle efficiency        accordingly (e.g., driving at night requires more energy, and is        therefore less efficient, due to the use of driving lights).        Sixth, by monitoring traffic conditions, for example using the        vehicle's GPS system, controller 107 can adjust the vehicle's        efficiency to take into account traffic conditions (e.g., slow        traffic requires more time to reach a given destination, thus        decreasing vehicle efficiency). Seventh, by determining the        driver (via direct input or utilizing a driver identification        system such as a keyfob that remotely identifies the user),        auxiliary system use (e.g., vehicle entertainment system,        lighting, etc.) for the particular user can be used to adjust        the vehicle's efficiency to take into account expected auxiliary        system loads. Eighth, by monitoring weather conditions, for        example using the vehicle's navigation system or broadcast        weather alerts, controller 107 can adjust the vehicle's        efficiency to take into account changing weather conditions that        may impact vehicle efficiency (e.g., heavy rains, snow, heavy        winds, etc.).

It will be appreciated that while the illustrated embodiments arepreferred, a variety of variations are envisioned that are clearlywithin the scope of the invention. For example, and as previously noted,much of the description and illustrated embodiments are based on theassumption that the non-metal-air battery pack is more energy efficiencyand has a better cycle life than the metal-air battery pack. While thatis typically true utilizing current battery chemistries andconfigurations, the advantages and disadvantage of non-metal-air andmetal-air batteries may be reversed in the future, leading to preferredembodiments of the invention optimizing the power source by minimizinguse of the non-metal-air battery pack.

Additionally, while both the metal-air battery pack 101 and thenon-metal-air battery pack 103 are shown and described as singularpacks, it should be understood that one or both of these packs may becomprised of multiple modules, and that the present invention is equallyapplicable to such a configuration. The use of multiple modules (ormini-battery packs) may be useful in distributing weight throughout EV100, or to fit into the physical constraints of the EV's chassis/body,and does not impact the present invention.

As will be understood by those familiar with the art, the presentinvention may be embodied in other specific forms without departing fromthe spirit or essential characteristics thereof. For example, while theillustrated embodiments assume the use of a non-metal-air battery packas the first battery pack and a metal-air battery pack as the secondbattery pack, these battery types may be reversed, thus using themetal-air battery pack as the first battery pack and the non-metal-airbattery pack as the second battery pack. Accordingly, the disclosuresand descriptions herein are intended to be illustrative, but notlimiting, of the scope of the invention which is set forth in thefollowing claims.

What is claimed is:
 1. A method of optimizing a power source utilized byan electric vehicle, the power source including at least a first batterypack and a second battery pack, wherein the first and second batterypacks are comprised of different battery types, the method comprisingthe steps of: a) determining a first state-of-charge (SOC) of the firstbattery pack and a second SOC of the second battery pack; b) determininga vehicle efficiency, wherein said vehicle efficiency corresponds to anestimated efficiency for converting an output from said power source tomiles traveled; c) obtaining a travel distance, wherein said traveldistance corresponds to an expected distance to travel before asubsequent charging cycle; d) determining an optimal split between saidfirst and second battery packs based on said vehicle efficiency and saidfirst and second SOCs, wherein said step of determining said optimalsplit further comprises the step of minimizing use of said secondbattery pack while still providing the electric vehicle with sufficientpower to traverse said travel distance; e) providing power to saidelectric vehicle from said power source in accordance with said optimalsplit; f) monitoring a first battery pack current SOC and a secondbattery pack current SOC; g) comparing said first battery pack currentSOC to a first battery pack predicted use profile and said secondbattery pack current SOC to a second battery pack predicted use profile;h) determining a revised optimal split between said first and secondbattery packs to reach said travel distance if said first battery packcurrent SOC does not match said first battery pack predicted use profilewithin a first preset tolerance or said second battery pack current SOCdoes not match said second battery pack predicted use profile within asecond preset tolerance, wherein said step of determining said revisedoptimal split further comprises the step of updating said vehicleefficiency and said travel distance; and i) providing power to saidelectric vehicle from said power source in accordance with said revisedoptimal split.
 2. The method of claim 1, wherein said first battery packis comprised of a plurality of non-metal-air cells and said secondbattery pack is comprised of a plurality of metal-air cells.
 3. Themethod of claim 1, wherein said step of determining said optimal splitfurther comprises the step of maintaining a minimum SOC within saidfirst battery pack.
 4. The method of claim 1, wherein said step ofdetermining said optimal split further comprises the step of maximizingpower source efficiency.
 5. The method of claim 1, wherein prior toperforming step h), said method further comprises the steps ofmonitoring traffic conditions and adjusting said vehicle efficiencybased on said traffic conditions.
 6. The method of claim 1, furthercomprising the step of determining first battery pack operationalparameters and second battery pack operational parameters prior toperforming step d).
 7. The method of claim 1, wherein said vehicleefficiency is defined as an average conversion efficiency for saidelectric vehicle.
 8. The method of claim 1, wherein said vehicleefficiency is given as a function of vehicle speed and vehicleacceleration.
 9. The method of claim 1, wherein said vehicle efficiencycorresponds to a particular driver, wherein said particular driver iscurrently operating said electric vehicle.
 10. The method of claim 1,wherein said travel distance corresponds to a preset distance, saidpreset distance corresponding to an average distance traveled betweencharge cycles for said electric vehicle.
 11. The method of claim 1,wherein said step of obtaining said travel distance further comprisesthe step of determining said travel distance from a destination inputinto a navigation system corresponding to said electric vehicle.
 12. Themethod of claim 1, wherein said step of obtaining said travel distancefurther comprises the step of determining said travel distance from atravel itinerary input into a navigation system corresponding to saidelectric vehicle.
 13. The method of claim 12, wherein prior toperforming step d), said method further comprises the steps ofestimating variations in vehicle elevation expected by said travelitinerary and adjusting said vehicle efficiency based on said variationsin vehicle elevation.
 14. The method of claim 12, wherein prior toperforming step d), said method further comprises the steps ofestimating traffic conditions expected by said travel itinerary andadjusting said vehicle efficiency based on said traffic conditions. 15.The method of claim 1, wherein said step of obtaining said traveldistance further comprises the step of inputting said travel distanceinto a user interface corresponding to said electric vehicle.
 16. Themethod of claim 1, wherein prior to performing step d), said methodfurther comprises the steps of determining ambient temperature,estimating first and second battery pack cooling demands based on saidambient temperature, and adjusting said vehicle efficiency based on saidfirst and second battery pack cooling demands.
 17. The method of claim1, wherein prior to performing step d), said method further comprisesthe steps of estimating vehicle weight and adjusting said vehicleefficiency based on said vehicle weight.
 18. The method of claim 1,wherein prior to performing step d), said method further comprises thesteps of determining ambient lighting conditions, estimating drivinglight requirements based on said ambient lighting conditions, estimatingfirst and second battery pack loading to meet said driving lightrequirements, and adjusting said vehicle efficiency based on said firstand second battery pack loading.
 19. The method of claim 1, whereinprior to performing step d), said method further comprises the steps ofdetermining weather conditions and adjusting said vehicle efficiencybased on said weather conditions.
 20. The method of claim 1, whereinprior to performing step d), said method further comprises the steps ofidentifying a driver for said electric vehicle, estimating auxiliarybattery pack loading corresponding to said driver based on prior use ofsaid electric vehicle by said driver, and adjusting said vehicleefficiency based on said estimated auxiliary battery pack loading.
 21. Amethod of optimizing a power source utilized by an electric vehicle, thepower source including at least a first battery pack and a secondbattery pack, wherein the first and second battery packs are comprisedof different battery types, the method comprising the steps of: a)determining a first state-of-charge (SOC) of the first battery pack anda second SOC of the second battery pack; b) determining a vehicleefficiency, wherein said vehicle efficiency corresponds to an estimatedefficiency for converting an output from said power source to milestraveled; c) obtaining a travel distance, wherein said travel distancecorresponds to an expected distance to travel before a subsequentcharging cycle; d) determining an optimal split between said first andsecond battery packs based on said vehicle efficiency and said first andsecond SOCs, wherein said step of determining said optimal split furthercomprises the step of minimizing use of said second battery pack whilestill providing the electric vehicle with sufficient power to traversesaid travel distance; e) providing power to said electric vehicle fromsaid power source in accordance with said optimal split; f) monitoring afirst battery pack current SOC and a second battery pack current SOC; g)determining a first battery pack remaining SOC and a second battery packremaining SOC; h) comparing said first battery pack remaining SOC to afirst battery pack predicted use profile and said second battery packremaining SOC to a second battery pack predicted use profile; i)determining a revised optimal split between said first and secondbattery packs to reach said travel distance if said first battery packremaining SOC does not match said first battery pack predicted useprofile within a first preset tolerance or said second battery packremaining SOC does not match said second battery pack predicted useprofile within a second preset tolerance, wherein said step ofdetermining said revised optimal split further comprises the step ofupdating said vehicle efficiency and said travel distance; and j)providing power to said electric vehicle from said power source inaccordance with said revised optimal split.
 22. The method of claim 21,wherein prior to performing step i), said method further comprises thesteps of monitoring traffic conditions and adjusting said vehicleefficiency based on said traffic conditions.
 23. The method of claim 21,wherein said first battery pack is comprised of a plurality ofnon-metal-air cells and said second battery pack is comprised of aplurality of metal-air cells.
 24. The method of claim 21, wherein saidstep of determining said optimal split further comprises the step ofmaintaining a minimum SOC within said first battery pack.
 25. The methodof claim 21, wherein said step of determining said optimal split furthercomprises the step of maximizing power source efficiency.
 26. The methodof claim 21, further comprising the step of determining first batterypack operational parameters and second battery pack operationalparameters prior to performing step d).
 27. The method of claim 21,wherein said vehicle efficiency is defined as an average conversionefficiency for said electric vehicle.
 28. The method of claim 21,wherein said vehicle efficiency is given as a function of vehicle speedand vehicle acceleration.
 29. The method of claim 21, wherein saidvehicle efficiency corresponds to a particular driver, wherein saidparticular driver is currently operating said electric vehicle.
 30. Themethod of claim 21, wherein said travel distance corresponds to a presetdistance, said preset distance corresponding to an average distancetraveled between charge cycles for said electric vehicle.
 31. The methodof claim 21, wherein said step of obtaining said travel distance furthercomprises the step of determining said travel distance from adestination input into a navigation system corresponding to saidelectric vehicle.
 32. The method of claim 21, wherein said step ofobtaining said travel distance further comprises the step of determiningsaid travel distance from a travel itinerary input into a navigationsystem corresponding to said electric vehicle.
 33. The method of claim32, wherein prior to performing step d), said method further comprisesthe steps of estimating variations in vehicle elevation expected by saidtravel itinerary and adjusting said vehicle efficiency based on saidvariations in vehicle elevation.
 34. The method of claim 32, whereinprior to performing step d), said method further comprises the steps ofestimating traffic conditions expected by said travel itinerary andadjusting said vehicle efficiency based on said traffic conditions. 35.The method of claim 21, wherein said step of obtaining said traveldistance further comprises the step of inputting said travel distanceinto a user interface corresponding to said electric vehicle.
 36. Themethod of claim 21, wherein prior to performing step d), said methodfurther comprises the steps of determining ambient temperature,estimating first and second battery pack cooling demands based on saidambient temperature, and adjusting said vehicle efficiency based on saidfirst and second battery pack cooling demands.
 37. The method of claim21, wherein prior to performing step d), said method further comprisesthe steps of estimating vehicle weight and adjusting said vehicleefficiency based on said vehicle weight.
 38. The method of claim 21,wherein prior to performing step d), said method further comprises thesteps of determining ambient lighting conditions, estimating drivinglight requirements based on said ambient lighting conditions, estimatingfirst and second battery pack loading to meet said driving lightrequirements, and adjusting said vehicle efficiency based on said firstand second battery pack loading.
 39. The method of claim 21, whereinprior to performing step d), said method further comprises the steps ofdetermining weather conditions and adjusting said vehicle efficiencybased on said weather conditions.
 40. The method of claim 21, whereinprior to performing step d), said method further comprises the steps ofidentifying a driver for said electric vehicle, estimating auxiliarybattery pack loading corresponding to said driver based on prior use ofsaid electric vehicle by said driver, and adjusting said vehicleefficiency based on said estimated auxiliary battery pack loading.